Research

ORCID iD

Research Interests

  • Computational Social Science
  • Data Science
  • Decision Science
  • E-Government
  • Machine Learning
  • Public Administration
  • Public Policy
  • Research Methods
  • Social Media Research

Doctoral Research

Valdosta State University IRB 04125-2021 Protocol Exemption Approval

Quantitative Research – A Data Science and Machine Learning Approach to Comparative COVID-19 Policy Responses – (January 2021-Present)

  • Study aim is to employ Data Science and Machine Learning in the investigation of health and economic policy responses to the COVID-19 pandemic in developed countries and developing countries approved by Valdosta State University IRB.
  • Prior research indicated more rigorous research is needed for contribution to the body of knowledge from a Public Policy perspective.
  • Examining literature for the development of the research design and execution to include two-tailed (non-directional) research hypotheses, null hypothesis, independent variable(s), dependent variable(s), etc.
  • Research aim is to answer how does Data Science and Machine Learning can inform Public Policy about the COVID-19 pandemic, what is the state of the COVID-19 pandemic in developed countries and developing countries, and empirically assess the correlation between policy responses and the state of COVID-19 in select countries.
  • Employing secondary data from multiple data sources.
  • Data wrangling, data analysis, and data visualization will be executed in RStudio.
  • Initial data analysis will include time series analysis, text mining, sentiment analysis, and spatial data analysis.
  • Creating R Scripts for reproducibility of data wrangling, data analysis, and data visualization in RStudio.

Valdosta State University IRB 03979-2020 Protocol Exemption Approval

Quantitative Research – Measuring the Effectiveness of E-Government Delivery Models in Developed Countries and Developing Countries – (January 2020-Present)

  • Measuring the effectiveness of E-Government delivery models in developed countries and developing countries is the second phase of the quantitative study measuring the effectiveness of e-government delivery models approved by Valdosta State University IRB.
  • Examined literature for the development of the research design and execution.
  • Research aim is to answer what is the state of e-government delivery models in developed countries and developing countries.
  • Employing secondary data from the first study phase plus developing two new datasets for data analysis.
  • Integrating data science and machine learning algorithms for data analysis in R.
  • Developed two-tailed research hypotheses (H1, H2, H3, H4, H5, H6) and null hypothesis (H0).
  • Creating R Scripts for reproducibility of data wrangling, data analysis, and data visualization in RStudio.
  • Performing descriptive statistics with summary function and describe function.
  • Creating boxplots to visually depict outliers with boxplot function and identified outliers with boxplot.stats function.
  • Employing ANOVA with the aov function to identify significant differences and Tukey’s Honest Significant Differences with the TukeyHSD function to identify the location of significant differences.
  • Employing K-means clustering with the kmeans function to cluster similar observations into groups and Hierarchal clustering with the hclust function to group observations based on similarities. Creating a dendrogram to depict the hierarchal relationships of the clusters as a tree diagram.

Quantitative Research – Measuring the Effectiveness of E-Government Delivery Models from a Public Administration Perspective – (August 2019-Present)

  • Examined literature for the development of the research design and execution.
  • Research aim is to answer what is the state of e-government delivery models globally.
  • Employing a quantitative longitudinal design utilizing secondary data.
  • Developed two-tailed research hypotheses (H1, H2, H3, H4, H5, H6) and null hypothesis (H0).
  • Imported datasets into RStudio with read_csv function.
  • Loaded R packages: tidyverse, psych, stats, rmarkdown.
  • Created tibbles with as_tibble function.
  • Performed descriptive statistics with summary function and describe function.
  • Performed Pearson correlation analysis with corr.test function, created correlation matrix with round(cor) function, and depicted correlation analysis on scatterplots with plot function.
  • Created boxplots to visually depict outliers with boxplot function and identified outliers with boxplot.stats function.
  • Created six simple linear regression models and two multiple linear regression models with lm function.
  • Created regression diagnostic plots (residuals vs fitted, normal q-q, scale-location, residuals vs leverage) for each regression model with par function and plot function.
  • Creating R Scripts for reproducibility of data wrangling, data analysis, and data visualization in RStudio.
  • Preparing for scholarly publications and presentations.
  • Citing R, RStudio, and R packages with citation function and RStudio.Version function for scholarly publications and presentations.

Independent Research

Quantitative Research – A Public Administration Research Approach and Empirical Perspective of COVID-19 – (March 2020-present)

  • Examining literature for the development of the research design and execution.
  • Research aim is to employ data science and machine learning algorithms in a public administration research approach and empirical perspective of COVID-19.
  • Time series analysis will be employed in R to examine the economic impact of COVID-19 related policies.
  • Text mining will be employed in R of COVID-19 research literature for text processing, modeling, analysis, and visualization.
  • Naïve Bayes will be employed in R for text classification.
  • Sentiment analysis will be employed in R to examine public opinion of COVID-19 related policies.
  • Spatial analysis will be employed in R of COVID-19 longitude and latitude cases.

Quantitative Research – Integration, Challenges, and Future Direction of Data Science in Public Administration – (August 2019-present)

  • Examining literature for the development of the research design and execution.
  • Research aim is to answer what is the state of data science in public administration.
  • Integrating data science and machine learning algorithms for data analysis in R.
  • Loaded R packages: tidyverse, psych, stats, rmarkdown.
  • Creating R Scripts for reproducibility of data wrangling, data analysis, and data visualization in RStudio.
  • Initial data collected and wrangling dataset A with 23,859 observations and dataset B with 16,716 observations in R
  • Importing datasets with read_csv function in RStudio.
  • Creating tibbles with as_tibble function.
  • Skipping rows with skip = function.
  • Deleting variables with select function.
  • Combining columns with unite function.
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